1. Ass. Prof. Dr. Mogeeb Mosleh
RESEARCH METHODOLOGY
Ass. Prof. Dr. Mogeeb Mosleh

2. Data analysis and interpretation
Think about analysis EARLY
Start with a plan
Code, enter, clean
Analyze
Interpret
Reflect
What did we learn?
What conclusions can we draw?
What are our recommendations?
What are the limitations of our analysis?

3. Why do I need an analysis plan?
To make sure the questions and your data collection instrument will get the information you want.
To align your desired “report” with the results of analysis and interpretation.
To improve reliability--consistent measures over time.

4. Key components of a data analysis plan
Purpose of the evaluation
Questions
What you hope to learn from the question
Analysis technique
How data will be presented

5. Quantitative Data Analysis
Ass. Prof. Dr. Mogeeb Mosleh

6. Analyzing and Interpreting Quantitative Data
Quantitative Data is
Presented in a numerical format
Collected in a standardized manner
e.g. surveys, closed-ended interviews, tests
Analyzed using statistical techniques

7. True or False?
Quantitative data we gather in Extension are more generalizable than qualitative data.
Stating limitations weakens the evaluation

8. Analyzing Survey Data Do you want to report…
how many people answered a, b, c, d?
the average number or score?
a change in score between two points in time?
how people compared?
how many people reached a certain level?

9. Common descriptive statistics
Count (frequencies)
Percentage
Mean
Mode
Median
Range
Standard deviation
Variance
Ranking

10. Other Statistics Statistical Significance Factor Analysis Etc.
Not often used in Extension program evaluation— generally require randomization, large samples, and/or control groups

11. Getting your data ready
Assign a unique identifier
Organize and keep all forms (questionnaires, interviews, testimonials)
Check for completeness and accuracy
Remove those that are incomplete or do not make sense

12. Getting the Data Ready for Analysis
Data coding: assigning a number to the participants’ responses so they can be entered into a database.
Data Entry: after responses have been coded, they can be entered into a database. Raw data can be entered through any software program (e.g., SPSS)

13. Editing Data
An example of an illogical response is an outlier response. An outlier is an observation that is substantially different from the other observations.
Inconsistent responses are responses that are not in harmony with other information.
Illegal codes are values that are not specified in the coding instructions.

14. Transforming Data

15. Getting a Feel for the Data

16. Frequencies

17. Descriptive Statistics: Central Tendencies and Dispersions

18. Reliability Analysis

19. Qualitative Data Analysis
Ass. Prof. Dr. Mogeeb Mosleh

20. Analyzing and Interpreting Qualitative Data
Qualitative data is thick in detail and description.
Data often in a narrative format
Data often collected by observation, open-ended interviewing, document review
Analysis often emphasizes understanding phenomena as they exist, not following pre- determined hypotheses

21. Qualitative Data Qualitative data: data in the form of words.
Examples: interview notes, transcripts of focus groups, answers to open-ended questions, transcription of video recordings, accounts of experiences with a product on the internet, news articles, and the like.

22. Analysis of Qualitative Data
The analysis of qualitative data is aimed at making valid inferences from the often overwhelming amount of collected data.
Steps:
data reduction
data display
drawing and verifying conclusions

23. Data Reduction
Coding: the analytic process through which the qualitative data that you have gathered are reduced, rearranged, and integrated to form theory.
Categorization: is the process of organizing, arranging, and classifying coding units.

24. Data Display
Data display: taking your reduced data and displaying them in an organized, condensed manner.
Examples: charts, matrices, diagrams, graphs, frequently mentioned phrases, and/or drawings.

25. Drawing Conclusions
At this point where you answer your research questions by determining what identified themes stand for, by thinking about explanations for observed patterns and relationships, or by making contrasts and comparisons.

26. Reliability in Qualitative Research
Category reliability “depends on the analyst’s ability to formulate categories and present to competent judges definitions of the categories so they will agree on which items of a certain population belong in a category and which do not.” (Kassarjian, 1977, p. 14).
Interjudge reliability can be defined degree of consistency between coders processing the same data (Kassarjian 1977).

27. Validity in Qualitative Research
Validity refers to the extent to which the qualitative research results:
accurately represent the collected data (internal validity)
can be generalized or transferred to other contexts or settings (external validity).